A Seasonal to Decadal Calibration of 1990–2100 Eastern Canadian Freshwater Discharge Simulations by Observations, Data Models, and Neural Networks
Bibliographic record
Abstract
A configuration of the NCAR WRF-Hydro model was sought using well established data models to guide the initial hydrologic model setup, as well as a seasonal streamflow post-processing by neural networks. Discharge was simulated using an eastern Canadian river network at two-km resolution. The river network was taken from a digital elevation model that was made to conform to observed catchment boundaries. Perturbations of a subset of model parameters were examined with reference to streamflow from 25 gauged catchments during the 2019 warm season. A data model defines the similarity of modelled streamflow to observations, and improvements were found in about half the individual catchments. With reference to 183 gauged catchments (1990–2022), further improvements were obtained at monthly and annual scales by neural network post-processing that targets all catchments at once as well as individual catchments.This seasonal calibration was applied to uncoupled WRF-Hydro simulations for the 1990–2100 warming period. Historic and future forcing were provided, respectively, by a European Centre for Medium-Range Weather Forecasting reanalysis (ERA5), and by a WRF atmospheric model downscaling of a set of Coupled Model Intercomparison Project (CMIP) models, where the latter were also seasonally calibrated. Eastern Canadian freshwater discharge peaks at about 105m3s−1, and as previous studies have shown, there is a trend toward increasing low flows during the cold season and an earlier peak discharge in spring. By design, neural networks yield more precise estimates by compensating for different hydrologic process representations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".